Facilitation or Competition? Tree Effects on Grass Biomass ... - PLOS

Facilitation or Competition? Tree Effects on Grass Biomass across a Precipitation Gradient Aristides Moustakas1,2*, William E. Kunin1, Tom C. Cameron1,3, Mahesh Sankaran1,4 1 Institute of Integrative and Comparative Biology, Faculty of Biological Sciences, University of Leeds, Leeds, United Kingdom, 2 School of Biological and Chemical Sciences, Queen Mary, University of London, London, United Kingdom, 3 Department of Ecology and Environmental Science, Umea˚ University, Umea˚, Sweden, 4 National Centre for Biological Sciences, TIFR, GKVK Campus, Bangalore, India

Abstract Savanna ecosystems are dominated by two distinct plant life forms, grasses and trees, but the interactions between them are poorly understood. Here, we quantified the effects of isolated savanna trees on grass biomass as a function of distance from the base of the tree and tree height, across a precipitation gradient in the Kruger National Park, South Africa. Our results suggest that mean annual precipitation (MAP) mediates the nature of tree-grass interactions in these ecosystems, with the impact of trees on grass biomass shifting qualitatively between 550 and 737 mm MAP. Tree effects on grass biomass were facilitative in drier sites (MAP#550 mm), with higher grass biomass observed beneath tree canopies than outside. In contrast, at the wettest site (MAP = 737 mm), grass biomass did not differ significantly beneath and outside tree canopies. Within this overall precipitation-driven pattern, tree height had positive effect on sub-canopy grass biomass at some sites, but these effects were weak and not consistent across the rainfall gradient. For a more synthetic understanding of tree-grass interactions in savannas, future studies should focus on isolating the different mechanisms by which trees influence grass biomass, both positively and negatively, and elucidate how their relative strengths change over broad environmental gradients. Citation: Moustakas A, Kunin WE, Cameron TC, Sankaran M (2013) Facilitation or Competition? Tree Effects on Grass Biomass across a Precipitation Gradient. PLoS ONE 8(2): e57025. doi:10.1371/journal.pone.0057025 Editor: Harald Auge, Helmholtz Centre for Environmental Research – UFZ, Germany Received June 6, 2012; Accepted January 21, 2013; Published February 22, 2013 Copyright: ß 2013 Moustakas et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: This research was funded by a NERC Research Grant to MS, WEK, and AM (NE-E017436-1). TCC was funded by the University of Leeds. MS would also like to acknowledge support provided by the Ramalingaswami Re-entry Fellowship, Department of Biotechnology, Government of India. AM thanks the School of Biological and Chemical Sciences, Queen Mary University of London and the Head of School, Matthew Evans for covering the publications fees. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: The authors have declared that no competing interests exist. * E-mail: [email protected]

savannas has not yet been fully explored (but see [7,21,22,23,24,25]). While it is well established that trees in savannas compete with grasses for light, nutrients, and water [6,26], there are also several cases where trees have been reported to have facilitative effects on grasses [21,23,27,28]. For example, grass biomass has been found to be higher under tree canopies when compared to the interspaces between trees in several systems [21,28,29,30,31,32]. However, grass biomass has also been reported to be lower beneath tree canopies in some savannas [31,33,34], while other studies have found no differences in grass biomass beneath tree canopies and in tree interspaces [35]. At present, the reasons underlying these divergent responses are unclear. Savanna trees can facilitate grasses by altering resource availability and microclimatic conditions, and providing grasses refuges from grazing. Trees in particular affect water redistribution in the landscape [24], and play important roles by creating shade [21,23,36] and by drawing water from deep sources inaccessible to grasses, i.e. hydraulic lift [21,23,35]. However, the extent to which such positive effects offset the negative effects of competition is unclear. For example, in a study of hydraulic lift (the process of water movement from relatively wet to dry soil layers through plant roots) of Acacia tortilis it was found that the d18O of water extracted from the xylem water of grasses indicated that when they grew near trees they had values similar to those of groundwater

Introduction Savannas are ecosystems characterised by a continuous grass layer and a discontinuous tree layer. They cover over 20% of the Earth’s total land surface and support a significant proportion of the planet’s livestock and wild herbivores [1,2]. The ratio of trees to grasses in savannas can vary depending on several environmental factors with precipitation and soil properties generally considered the predominant drivers at large scales [1,2,3,4,5] which in turn modulate plant-plant interactions at local scales [6,7,8]. Traditionally, ecologists have emphasized the role of competition between trees and grasses as being a key determinant of savanna structure [1,9,10]. While the importance of competition in structuring ecological communities is widely recognized, there is also a growing appreciation of the role of facilitation amongst plants in structuring ecological communities [11,12,13,14], especially in stressful environments [15,16]; indeed facilitation is a process that needs to be more integrated into ecological theory [17]. Facilitation can occur through various mechanisms including refuge from physical stress [18], refuge from predation [17], refuge from competition [19], and improved resource availability [20]. At present, the importance of tree-grass facilitation relative to competition, and the role of microhabitats and microclimatic factors created by the presence or absence of trees for grasses in

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February 2013 | Volume 8 | Issue 2 | e57025

Spatial Properties of Tree-Grass Interactions

from the Administration, Scientific Services and the local Rangers of the Kruger National Park.

either because grasses could use water from deeper soils or because they used water hydraulically lifted by trees [37]. However, at the same site [35] found lower soil moisture content under trees than in the open, both during dry and wet seasons, and marginally higher grass biomass in open areas. Thus, while hydraulic lift did facilitate water uptake by grasses, the effects of competition with tree roots cancelled the beneficial influence of tree roots on grass biomass at that site [38]. Overall, findings in savannas have shown that both facilitation and competition can occur in the same ecosystem, and that competitive and facilitative effects do not always balance [39]. In this study, we examined the effects of isolated trees on grass biomass across a precipitation gradient in an African savanna. Specifically, we looked at how grass biomass changed as a function of distance from the base of the tree and with tree size, and how this relationship was influenced by precipitation. In arid and semiarid savannas where water is the main limiting resource [2,40], we expected trees to facilitate grasses by enhancing water availability, and predicted that grass biomass values would be higher in the sub-canopy areas than in tree interspaces. In contrast, in more mesic savannas, where water is typically less limiting and factors such as shading by trees becomes increasingly important, we expected grass biomass to be lower in sub-canopy areas than in the interspaces between trees. Further, since the microclimate experienced by grasses is also likely to be affected by individual tree characteristics such as size, we expected that the extent to which trees facilitate or compete with grasses would change with tree size. Increases in tree size can lead to increased soil resource availability and hence increased sub-canopy grass biomass as a result of hydraulic lift or increased nutrient contents below canopies [41]. Alternately, increases in tree size can also result in increased solar radiation and evapotranspiration in sub-canopy areas leading to lowered soil moisture and sub-canopy grass biomass [21,23]. We expected sub-canopy grass biomass to increase with tree size with such effects particularly pronounced in mesic areas where sub-canopy grasses tend to be light-limited, and where increased solar irradiation beneath larger trees can in fact have a positive effect on sub-canopy grass biomass.

Sampling methodology We identified isolated trees in each of the replicate plots (N = 16; four in each landscape). Isolated trees were defined as those for which distance to the nearest woody plant (tree or shrub) neighbour was as least three times the canopy radius of the focal tree. For each tree, we recorded height, girth at breast height, and canopy diameter along two perpendicular axes. In cases where measuring girth at breast height was not feasible we recorded their girth at the closest available point. In addition, we also measured grass biomass at different distances from the base of the tree, corresponding to 50%, 100% (i.e. canopy edge or drip line), 150% and 200% of the tree canopy radius [30]. Distances were measured as a proportion of canopy size rather than as absolute values in order to facilitate comparison of canopy effects on grass biomass of uneven-sized trees [30]. For each tree, grass biomass was measured along 3 transects radiating away from the base of tree, each 1200 apart from the other. Grass biomass measurements were taken at peak herbaceous standing crop in January and February. Biomass values from the three transects were averaged to get a mean value for grass biomass for each distance category for each tree. Grass biomass was estimated using a disc pasture meter [44] specifically calibrated for KNP [42,45]. Disc pasture meter calibrations, conducted using samples from across the full extent of KNP, indicate a high degree of concordance between measured and estimated values of grass biomass in KNP (R2 = 0.972, 45). Grass biomass was estimated from disk pasture pffiffiffi meter readings (in cm) using the formula y = 2301.9+226 x, where x is the disc reading in cm and y the grass biomass in g/m2 [45]. For a more detailed description of the device and its calibrations for KNP see [44,45]. In all, a total of 93 trees were sampled across all plots (Table 1). One plot (Numbi block in Pretoriuskop) had no isolated trees as per our criterion as a result of the dense nature of the vegetation in the plot.

Methods Study area

Table 1. Details of the study sites.

The study was conducted in the Kruger National Park (KNP), South Africa between January and February 2008. The park is situated in the savannas of north-eastern South Africa, and covers an area of ,19,633 km2. Altitude ranges from 260 to 839 m above sea level within the park. Mean Annual Precipitation (MAP) varies from around 750 mm in the south to approximately 350 mm in the north, with marked annual variations [42]. The vegetation in the park is characterized by dense savanna with species such as Acacia nigrescens, Sclerocarya birrea, Combretum imberbe, Colophospermum mopane, Terminalia sericea and Burkea africana dominating the canopy depending on the location within the park [42]. Our study was conducted in the long-term Experimental Burn Plots (EBPs) of the Kruger National Park [42]. Only ‘unburnt’ treatment plots were sampled (i.e. fire exclusion plots) for this study. Plots were located in four major landscapes of the park underlain by both granites: Pretoriuskop (MAP = 737 mm) and Skukuza (MAP = 550 mm), and basalts: Satara (MAP = 544 mm) and Mopane (MAP = 496 mm). In each of the four landscapes, there were four replicate plots, each covering an area of ,7ha. Fire had been excluded from our study plots for more than 50 years [42,43]. All necessary permits for field work were obtained

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Site

MAP N

Height [StDev]

Dominant isolated tree species

Mopane

496

17 4.29 [1.53]

Colophospermum mopane (15)

Satara

544

28 5.87 [1.85]

Acacia nigrescens (19)

Skukuza

550

27 6.17 [1.65]

Acacia burkei (6) Sclerocarya birrea (10) Combretum apiculatum (4) Terminalia sericea (4) Combretum collinum (3) Pretoriuskop737

21 5.02 [1.30]

Terminalia sericea (20)

MAP is the mean annual precipitation of the site in mm. N is the number of isolated trees sampled at each site. Each site is replicated by four blocks. The mean height of sampled trees [Height] and standard deviation [StDev] in meters is also listed, as is the identity, and number sampled (in parentheses), of the dominant tree species at each site. The data gathered here have also been included in a larger dataset comprising similar data from Africa and North America as part of a larger-scale analysis of competitive-facilitative interactions in savannas [31]. doi:10.1371/journal.pone.0057025.t001

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Results

Statistical Analysis Linear mixed effects models were used to quantify the effects of tree characteristics on grass biomass across sites. Rainfall, distance from the base of the tree, and tree size were the factors included in the analysis (fixed effects), with geology (granite or basalt) included as a random effect in the model. We chose to include geology as a random rather than fixed effect because the design of the Experimental Burn Plots in KNP does not allow separating out the individual and interactive effects of both rainfall and geology simultaneously; the two lower rainfall sites (Mopane and Satara) are on basalts and the two higher rainfall sites (Skukuza and Pretoriuskop) are on granites. Our dataset also contained a different number of species at each site. Because sample sizes were limited, we were not able to explicitly test to see if tree effects on grass biomass varied depending on tree species identity. However, since tree species identity can potentially play a role in regulating tree-grass interactions in savannas [3,6,46] we additionally included tree species identity as a random effect in our analysis. An initial calculation of the contribution of the random structure (Standard Deviations of the random effects from our model) showed that the factor that contributes least to variance in the estimates of the mean grass biomass is geology (Random effects: ,1|geology, Intercept StdDev = 17.73082; ,1 | geology/plot, Intercept StdDev = 114.0258; ,1 | geology/plot/species, Intercept StdDev = 106.3738; ,1 | geology/plot/species/treeID, Intercept StdDev = 54.58133). A separate evaluation of the contribution of species identity using a linear NULL model, (ANOVA(biomass,plot/ species/treeID) where the nested structure is a fixed effect, showed that there is a greater contribution to the variance in grass biomass from plots within sites (s2 = 14646) or between individual trees within species (s2 = 24232) than between tree species (s2 = 3023). In the mixed effects model used, grass biomass data were grouped by distance increments within individual trees nested by species within plots, within geology to account for non-independence of data from trees on the same site [47]. Data grouping accounts for autocorrelation between samples in all its forms [47]. Thus, although we only report on the effects of trees on grass biomass independent of tree species identity, our analysis nevertheless accounted for the fact that there are differences in tree species composition across our study sites. We evaluated the effects of three correlated measures of tree size – height, basal area and canopy area – on grass biomass across sites. We created three different maximal models on nontransformed grass biomass with different combinations of the correlated fixed effects: tree height, canopy area and basal area (as indices of tree size) as well as distance from the base of the tree and site. The maximal models included all possible interaction terms. We used the Akaike Information Criterion (AIC) to assess the best maximal model. Of the three indices of tree size evaluated, the maximal model which included tree height as the index of tree size was the best, and we only report results from this model here. The maximal model which included tree height as the index of tree size was subsequently simplified via stepwise deletion wherein nonsignificant factors and their interactions were sequentially removed, until further simplification was not justified. Model selection was conducted using the AIC with maximum likelihood estimation, but the presented fit of the minimal model used restricted maximum likelihood (REML); [47]. Any deletion that did not increase AIC scores by more than 2 was deemed to be justified [48]. The minimum adequate model selected by AIC or comparative F-tests were identical. Inspection of residual plots for constancy of variance and heteroscedasticity indicated that the model was well behaved in all cases. All analyses were conducted in R 2.14.1 using the nlme package [49]. PLOS ONE | www.plosone.org

The minimum adequate model explaining grass biomass included the main effects of rainfall, distance and tree height and the two-way interactions between rainfall and distance, and rainfall and height (Table 2). Tree effects on grass biomass beneath canopies changed across the rainfall gradient. In the three drier sites (Mopane MAP = 494 mm, Satara MAP = 544 mm, & Skukuza MAP = 550 mm), grass biomass was significantly higher beneath tree canopies than outside canopies (Table 2 and Figure 1). In contrast, there were no significant differences in grass biomass beneath and outside canopies at the wettest site (Pretoriuskop, MAP = 737, Fig. 1). The interaction between rainfall and tree height was marginally significant (P = 0.053) suggesting that effects of tree height on grass biomass differed between sites (rainfall x height interaction, Table 2). At the driest site (Mopane), sub-canopy grass biomass increased as tree height increased (adjusted R2 = 21.1%, p = 0.039, Figure 2a). A similar pattern was observed at Skukuza (adjusted R2 = 9.9%, p = 0.048; Figure 2c). At the wettest site (Pretoriuskop) grass biomass below tree canopies increased with tree height but that relationship was only marginally significant (adjusted R2 = 10.2%, p = 0.055; Figure 2d). At Satara there was no effect of tree height on grass biomass (adjusted R2 = 0%, p = 0.975; Figure 2b). These observed patterns were not a consequence of consistent differences in tree architecture or height resulting from species turnover across sites; tree height distributions were similar across our four study sites (Figure 3a), with taller trees having proportionally larger canopies regardless of species identity (Figure 3b).

Discussion Our results indicate that the nature of tree-grass interactions changes from positive to negative across a gradient of increasing precipitation. We suggest that this change occurs due to a decline in the relative importance of facilitation of grasses by trees, relative to competition between them, with increasing rainfall. Table 2. ANOVA results of the most parsimonious linear fixed effects model.

Variable

numDF

denDF

F-value

p-value

(Intercept)

1

270

98.99

,.0001

Rainfall

3

54

14.31

,.0001

Distance

3

270

37.49

,.0001

Height

1

54

0.41

0.5241

Rainfall:Distance

9

270

8.51

,.0001

Rainfall:Height

3

54

2.72

0.0532*

Grass biomass is the dependent variable while fixed effects are distance from the base of the tree, tree height, and rainfall. The model includes random effects of individual trees, nested in tree species identity, nested within strings nested in geology (see statistical analysis). Significant p-values are bolded. The three-way interaction between Rainfall:Distance:Height as well as the two-way interaction between Distance:Height were included in the maximal model but were simplified as non significant. According to our results, tree height is not a significant contributor per se, but the interaction between Rainfall:Site is marginally significant. Thus, height is a potentially significant contributor depending on rainfall. Given the fact that Table 2 shows 2 two-way interactions as significant, (Rainfall:Distance and Rainfall:Height) we need two figures to assess this. Figure 1 shows the relationship between Rainfall:Distance and Figure 2 between Rainfall:Height. doi:10.1371/journal.pone.0057025.t002

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Figure 1. Grass biomass in each of the four MAP values replicated (corresponding to the four different study sites each replicated by four blocks) as a function of distance from the base of the tree (stem) corresponding to 50%, 100%, 150% and 200% of the tree canopy radius. Biomass values at 100% correspond to the canopy edge or drip line. Bars represent 62 standard errors. Grass biomass is greater beneath the canopy (50% distance) compared to outside the canopy at the three drier sites, but not at the wettest site. doi:10.1371/journal.pone.0057025.g001

According to our results, the net impact of trees on grass biomass appears to shift qualitatively between 550 (Skukuza) and 737 (Pretoriuskop) mm MAP (Table 1 and Figure 1) in our study site. This is in accordance with the results of previous studies showing that tree effects on grass biomass are more positive on arid sites than in mesic ones [23,27]. Grass biomass has been reported to be higher below tree canopies in more arid savannas (MAP,,650 mm; e.g. [21,28,30]), and lower below tree canopies in more mesic sites (MAP.,650 mm; e.g. [34,50]). In sites with intermediate rainfall (MAP